{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy.ma as ma\n",
    "import csv\n",
    "\n",
    "from numpy.random import uniform, seed\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "# This import registers the 3D projection, but is otherwise unused.\n",
    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\n",
    "from matplotlib.collections import PolyCollection\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "from scipy.interpolate import CubicSpline, UnivariateSpline, griddata\n",
    "# from scipy.signal import savgol_filter\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def bin_data(xi, yi):\n",
    "    x = np.unique(xi)\n",
    "    y = [None] * x.size\n",
    "    for i in range(x.size): y[i] = yi[xi == x[i]]\n",
    "    # Return \n",
    "    y_mean = np.array([a.mean() for a in y])\n",
    "    y_err = np.array([np.std(a) / (a.size**0.5) for a in y])\n",
    "    return (x, y_mean, y_err, y, xi, yi)\n",
    "\n",
    "\n",
    "\n",
    "def cc(arg):\n",
    "    '''\n",
    "    Shorthand to convert 'named' colors to rgba format at 60% opacity.\n",
    "    '''\n",
    "    return mcolors.to_rgba(arg, alpha=0.6)\n",
    "\n",
    "\n",
    "def polygon_under_graph(xlist, ylist):\n",
    "    '''\n",
    "    Construct the vertex list which defines the polygon filling the space under\n",
    "    the (xlist, ylist) line graph.  Assumes the xs are in ascending order.\n",
    "    '''\n",
    "    return [(xlist[0], 0.), *zip(xlist, ylist), (xlist[-1], 0.)]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataRead=sio.loadmat('Fig1Data.mat',squeeze_me=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from types import SimpleNamespace \n",
    "data = SimpleNamespace(**DataRead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_OD=pd.DataFrame(DataRead['OD_img'])\n",
    "df_OD.to_csv('Fig1F_2Dimg.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dT=pd.DataFrame(data.DT_img)\n",
    "df_dT.to_csv('Fig1G_2DdT.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nz=pd.DataFrame({'z': data.nz_x, 'nz': data.nz_y.T})\n",
    "df_nz.to_csv('Fig1F_1Dprofile.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dTz=pd.DataFrame({'z': data.Tz_bin_x, 'dTz': data.Tz_bin_y,'dTz_err':data.Tz_bin_y_err})\n",
    "df_dTz.to_csv('Fig1G_1Dprofile.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Fig1E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[2.5,5])\n",
    "\n",
    "ax=fig.add_axes([0.10,0.5,0.52,0.26])\n",
    "ax.tick_params(axis='both',length=2.5,direction='in',labelsize=9,pad=0)\n",
    "plt.sca(ax)\n",
    "plt.imshow(data.OD_img,cmap=plt.copper(),aspect=1,origin='lower')\n",
    "plt.clim([-0.2,10.2])\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "ax_cb=fig.add_axes([0.1,0.765,0.52,0.0075])\n",
    "ax_cb.tick_params(axis='both',direction='out',labelsize=9,length=1.5,top=False,bottom=False,left=True,pad=0)\n",
    "cb=plt.colorbar(cax=ax_cb,orientation='horizontal')\n",
    "ax_cb.xaxis.tick_top()\n",
    "ax_cb.xaxis.set_label_position('top')\n",
    "ax_cb.set_xlabel('Density (a.u.)',fontsize=9,labelpad=-2)\n",
    "cb.set_ticks([0,10])\n",
    "\n",
    "ax_cut=fig.add_axes([0.10,0.357,0.52,0.136])\n",
    "ax_cut.tick_params(axis='both',length=1.5,direction='out',labelsize=9,pad=1)\n",
    "\n",
    "plt.plot(data.nz_x,data.nz_y.T,color=[0.35,0.35,0.35],linewidth=0.75)\n",
    "plt.axvline(0,linestyle='--',linewidth=0.5,color='k')\n",
    "plt.axvline(data.L,linestyle='--',linewidth=0.5,color='k')\n",
    "\n",
    "\n",
    "plt.xlim([-(73*2.1-data.L)/2,data.L+(73*2.1-data.L)/2])\n",
    "plt.ylim([-0.05,0.8])\n",
    "\n",
    "plt.xticks([0,90])\n",
    "\n",
    "plt.ylabel(r'$n$ ($\\mu \\mathrm{m}^{-3}$)',fontsize=9,labelpad=-1)\n",
    "plt.xlabel(r'z ($\\mu$m)',fontsize=9,labelpad=-5)\n",
    "\n",
    "fig.savefig('Fig1E.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fig1F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[2.5,5])\n",
    "\n",
    "ax=fig.add_axes([0.10,0.5,0.52,0.26])\n",
    "ax.tick_params(axis='both',length=2.5,direction='in',labelsize=9,pad=0)\n",
    "plt.sca(ax)\n",
    "plt.imshow(data.DT_img,cmap='bwr',aspect=1,origin='lower')\n",
    "plt.clim([-5.5,5.5])\n",
    "# plt.text(-3,-12,r'50 $\\mu$m',fontsize=9)\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "\n",
    "ax_cb=fig.add_axes([0.1,0.765,0.52,0.0075])\n",
    "ax_cb.tick_params(axis='both',direction='out',labelsize=9,length=1.5,top=False,bottom=False,left=True,pad=0)\n",
    "\n",
    "cb=plt.colorbar(cax=ax_cb,orientation='horizontal')\n",
    "ax_cb.xaxis.tick_top()\n",
    "ax_cb.xaxis.set_label_position('top')\n",
    "cb.set_ticks([-5,5])\n",
    "ax_cb.set_xlabel('Temperature\\n Change (nK)',fontsize=9,labelpad=-2)\n",
    "\n",
    "\n",
    "\n",
    "ax_cut=fig.add_axes([0.10,0.357,0.52,0.136])\n",
    "\n",
    "ax_cut.tick_params(axis='x',length=1.5,direction='out',labelsize=9,pad=1)\n",
    "ax_cut.tick_params(axis='y',length=1.5,direction='out',labelsize=9,pad=1,right=False,left=True,labelright=False,labelleft=True)\n",
    "\n",
    "\n",
    "x_min=5\n",
    "x_max=87\n",
    "\n",
    "# plt.plot(movie_T[1][mask],movie_T[2][:,0][mask]*TF,'o',markersize=1.75,mfc=colorstyle['blue_face'],mec=colorstyle['blue_edge'],mew=0.35)\n",
    "plt.errorbar(data.Tz_bin_x,data.Tz_bin_y,yerr=data.Tz_bin_y_err,linestyle='None',marker='o',\n",
    "            markersize=2.75,mfc=colorstyle['gray_edge'],mec='k',mew=0.5,\n",
    "             ecolor='k',elinewidth=0.5)\n",
    "\n",
    "plt.plot(data.zline,data.dTline,'--',color=colorstyle['gray_edge'],linewidth=0.5)\n",
    "\n",
    "plt.axvline(0,linestyle='--',linewidth=0.5,color='k')\n",
    "plt.axvline(data.L,linestyle='--',linewidth=0.5,color='k')\n",
    "\n",
    "plt.xlim([-(73*2.1-data.L)/2,data.L+(73*2.1-data.L)/2])\n",
    "plt.ylim([-6.,6.])\n",
    "\n",
    "plt.xticks([0,90])\n",
    "plt.yticks([-4,0,4])\n",
    "\n",
    "plt.ylabel(r'$\\Delta T$ (nK)',fontsize=9,labelpad=2)\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "\n",
    "plt.xlabel(r'z ($\\mu$m)',fontsize=9,labelpad=-5)\n",
    "\n",
    "fig.savefig('Fig1F.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  1.87456295,  3.7491259 ,  5.62368885,  7.4982518 ,\n",
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